Senior Product Manager, Platform Performance & Infrastructure

Sigma Computing Sigma Computing · Data AI · San Francisco, CA · Product

Senior Product Manager for an AI apps and analytics platform, focusing on foundational backend capabilities like query execution, caching, agent infrastructure, and observability. The role involves owning the product roadmap, driving execution, and working closely with engineering and enterprise customers to ensure platform performance, reliability, and scalability.

What you'd actually do

  1. Define and own the multi-quarter product roadmap for Sigma's platform performance and infrastructure surface area, including query execution, compute and caching, metadata services, compiler-related components, agent infrastructure, and connectors.
  2. Define the strategy and roadmap for agent monitoring capabilities, enabling customers and internal teams to observe, debug, and optimize AI agent behavior within Sigma's platform.
  3. Own platform observability infrastructure — including logging, tracing, alerting, and diagnostic tooling — ensuring Sigma's platform surfaces actionable insights to both engineering teams and enterprise customers.
  4. Partner closely with engineering to scope, prioritize, and ship improvements across latency, concurrency, throughput, and cost efficiency.
  5. Define product requirements across the full query lifecycle — from generation and compilation to execution, caching, and results delivery.

Skills

Required

  • 4+ years of product management experience in B2B SaaS, data platforms, analytics, infrastructure, or developer-facing products.
  • Demonstrated experience owning and delivering a product roadmap end-to-end across technically complex areas.
  • Strong understanding of databases, distributed systems, query engines, caching, APIs, and cloud infrastructure.
  • Proven track record of working directly with enterprise customers to understand needs and translate them into product decisions.

Nice to have

  • Experience with observability tooling, agent monitoring, or platform diagnostics is a strong plus.

What the JD emphasized

  • enterprise customers
  • performance challenges
  • enterprise pain points
  • platform investments that scale
  • technically strong
  • infrastructure tradeoffs
  • enterprise customers
  • performance pain points
  • high-scale use cases
  • enterprise scale
  • high-concurrency workloads
  • embedded analytics use cases
  • enterprise scale
  • enterprise accounts

Other signals

  • AI apps and analytics platform
  • agent infrastructure
  • agent monitoring
  • platform observability
  • enterprise customers
  • performance and infrastructure